GIBBS SAMPLING ON A STEADY MODEL

Abstract

In this study Gibbs sampling, a widely used simulation method, is ap-

plied to the steady model, a simple variation of the dynamic linear

model, and the model parameters are estimated. The estimates ob-

tained from Gibbs sampling and the results for the standard Kalman filter are compared and are found to be close. These similarities in the

results indicate the success of the stochastic simulation. In this study,

a variance modulation on the steady model is also applied and Gibbs

sampling is proposed to overcome analytic problems. In the variance

adaptation, defined as $a\mu_t^b(a,b>0)$  estimates for the model parame-

ters are obtained for different values of a and b.

Keywords

• Bayesian approach,BUGS,Gibbs sampling,Steady model,Variance modulation

References

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